Cover crops can positively impact productivity and the environment. While improved estimates of cover crop N benefits could help promote their adoption, little information is currently available at the broad scale. We conducted a multi-site study to determine whether use of satellite images and site factors could fill this gap. Six spectral indices were extracted from Sentinel-2 satellite imagery and used with modeling covariates to estimate cover crop properties and biomass N credits (biomass × N contents). The partial least squares regression (PLSR) models were calibrated and validated with samples from 42 cover crop fields located in the midwestern and northeastern United States collected in 2017–2018. Remote sensing (RS)-derived spectral indices strongly correlated (r >.7) with red clover (Trifolium pratense L.) but not with rye (Secale cereale L.) biomass. Growing degree days (GDDs), cover height, ground cover percentage, and temperature often had high importance (variable importance in projection >1) in PLSR models. Model predictive power was limited for estimates of biomass N credits when data from all validation sites and cover types were used (adjusted [adj] R2 =.52). However, models for both biomass (adj R2 =.81) and biomass N credits (adj R2 =.89) were successful for red clover fields. This suggests N benefits could be more effectively modeled for specific cover crop types. We also found RS-based estimation of C/N ratios performed moderately well when applied to the complete dataset (adj R2 =.54), suggesting a way to differentiate grass and legume cover crops that can potentially inform biogeochemical models.
ASJC Scopus subject areas
- Agronomy and Crop Science